Summary
In this chapter, we explored the critical aspect of detecting rare events and edge cases in machine learning. Rare events, by their infrequency, hold significant implications across various domains and necessitate special attention. We delved into several techniques and methodologies that equip us to effectively identify and handle these uncommon occurrences.
Statistical methods, such as Z-scores and IQR, provide powerful tools to pinpoint outliers and anomalies in our data. These methods aid in establishing meaningful thresholds for identifying rare events, enabling us to distinguish significant data points from noise.
We also explored machine learning-based anomaly detection techniques, such as isolation forest and autoencoders. These methods leverage unsupervised learning to identify patterns and deviations that diverge from the majority of the data, making them well suited for detecting rare events in complex datasets.
Additionally, we discussed the significance...
